Enhanced multi-verse optimizer for task scheduling in cloud computing environments
| Autor: | Seyedali Mirjalili, Amjad Hudaib, Rizik M. H. Al-Sayyed, Sarah E. Shukri |
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| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Job shop scheduling business.industry Computer science Distributed computing General Engineering Particle swarm optimization Cloud computing 02 engineering and technology computer.software_genre Execution time Computer Science Applications Scheduling (computing) 020901 industrial engineering & automation Artificial Intelligence Virtual machine 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business computer |
| Zdroj: | Expert Systems with Applications. 168:114230 |
| ISSN: | 0957-4174 |
| Popis: | Cloud computing is a trending technology that allows users to use computing resources remotely in a pay-per-use model. One of the main challenges in cloud computing environments is task scheduling, in which tasks should be scheduled efficiently to minimize execution time and cost while maximizing resources’ utilization. Many meta-heuristic algorithms are used for task scheduling in cloud environments in the literature such as Multi-Verse Optimizer (MVO) and Particle Swarm Optimization (PSO). In this paper, an Enhanced version of the Multi-Verse Optimizer (EMVO) is proposed as a superior task scheduler in this area. The proposed EMVO is compared with both original MVO and the PSO algorithms in cloud environments. The results show that EMVO substantially outperforms both MVO and PSO algorithms in terms of achieving minimized makespan time and increasing resources’ utilization. |
| Databáze: | OpenAIRE |
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